Tag Archives: business

Building with AI: Everything Except the Code

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Last month I wrote about my commitment to build Ascendius in public and share the journey as openly as possible. I’m finally getting the chance to begin that process properly – and I want to start by talking about something we don’t see enough of: building companies with AI from the business side, not the code side.

If you’re online, you’ve seen endless posts about “vibe-coding,” prompt-hacking, and engineering workflows supercharged by AI. They’re fun, useful, and often incredible. But they barely scratch the surface of the real transformation happening right now.

Because the most profound change isn’t how we write code and build products.

It’s how we build companies.

Why I’m Doing This

No one who’s built a great company did it without people before them “paying it forward” – and given how much of a transformation we’re all going through with AI, it feels like now is a time to pay it forward even more than before.

For all of my professional life, the internet has been the great transformer, giving us access to information and each other. But, even with its transformative information connectivity, we each still had to do all the actual thinking. AI changes that. We’ve entered what I call the era of Universal Intelligence – where high-level cognitive horsepower is no longer scarce or expensive.

What used to require hiring a McKinsey partner, a CFO, a market researcher, or a high-priced operator can now be achieved with a curious mind and a $20 subscription. Sure, it isn’t perfect and you still have to do the work and execute, but it obliterates the old barrier that said “only big companies can afford this level of intelligence.”

Small, high-growth companies are now positioned to win in ways they simply couldn’t before. And given small, high-growth companies create 70% of net new jobs, we need these “Gazelles” to come out the other side of this economic disruption successfully.

Having built teams the hard way before, I know how game-changing this shift is for founders, and I hope this series is helpful to many other entrepreneurs in this period of dramatic change.

AI Inverts the Pyramid

In the old world of software, computers were “lightning-fast idiots”: great at automating structured, repetitive tasks but useless anywhere you needed judgment or creativity. So businesses grew up with a simple model: completely automate the bottom layer of repetitive, rote work on the farm, in the factory, in a professional workflow, and rely completely on humans for everything on top.

Because it is probabilistic – not deterministic – it struggles with doing everything perfectly. But it excels at things technology could never do before – insight, analysis, planning, writing, strategy. AI flips the pyramid upside-down. The high-leverage work that used to be the protected domain of knowledge workers and senior leaders is now the easiest to accelerate. Meanwhile, all the messy, high-variance realities of running a business (people, conflict, culture, alignment, client expectations, humanity) remain stubbornly human.

And in a world full of vibe-coding posts and agentic this or that, this acceleration of everything else is the part not many people are writing about.

While critics (rightly) point out the examples where AI screws up, they’re missing the 80% of work technology has never touched before – the strategic, creative, managerial, operational, and unstructured analytical work that underpins building an actual company.

This inversion is where the real opportunity lives.

How I’ve Been Using AI to Build TeamScore

While building TeamScore this year, AI has been my researcher, strategist, planner, analyst, editor, sounding board, and sometimes my “partner in the room” when I needed to pressure-test decisions at pace.

It isn’t about agents – it’s about intelligence.

AI has helped me:

  • Brainstorm the original TeamScore idea and high level go-to-market (GTM plan)
  • Create detailed Ideal Customer Profiles (ICPs) and buyer Personas for TeamScore
  • Create and refine Brand Personalities for consistency in tone and messaging
  • Pressure-test the market thesis against real BLS and Census data
  • Tear apart a competitive landscape in a weekend
  • Implement a complete set of help guides in a weekend through automation and n8n workflows
  • Write dozens of internal strategy documents
  • Evaluate vendor economics and contract structures
  • Model risk, ops, and compliance decisions (including SOC2)

AI didn’t replace any of this critical work. But it did make it possible for one person to do the work of an entire early-stage team – and I think do it pretty well.

That’s what I want to share with you.

Because if the internet made the world flat, AI is making the world tall again. Ambitious founders can now punch impossibly far above their weight – not through headcount, but through intelligence leverage.

Where This Goes Next (and Your Invitation)

While I’ll be publishing these pieces weekly on my blog over the next few months, I’m also going to be sharing many more short video breakdowns and answering questions directly on LinkedIn – because apparently that is what we all do now.

If you’re a founder, operator, manager, or someone determined to build something meaningful – especially in this era of “Universal Intelligence” – I’d love you to come along for the ride.

I promise to keep it candid, practical, and occasionally a little feisty – join the conversation on LinkedIn.

Fifteen Years of Paying It Forward

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Every few months, I’m reminded just how special Startmate is – and Demo Day for the Winter 2025 cohort today is another one of those moments.

It’s a celebration not just of another set of ambitious founders, but of the community that has quietly shaped the startup landscape in Australia and New Zealand for the past decade and a half.

When Niki Scevak called and offered me the chance to invest in and mentor Australia’s first proper accelerator back in 2010, the local ecosystem was still young. There was talent everywhere – you could feel the energy at Silicon Beach meetups in Sydney – but we didn’t yet have the networks, mentors, and early-stage capital that make industries thrive.

From our cohort in 2011, it didn’t take long to see that Startmate was going to change that. It built the connective tissue our ecosystem needed: a program built on paying it forward, where experienced founders helped the next generation avoid the mistakes we’d already made. That simple idea and a lot of effort became a flywheel that’s still spinning – over 350 investments, companies now worth more than $4.5 billion, and a ripple effect that gave birth to Blackbird and a much bigger belief in what’s possible.

I’ve had the privilege of seeing that evolution up close – from the early, scrappy cohorts iterating, where each cohort made the pilgrimage to San Francisco and onto the well-oiled StartMate machine Batko leads today. Watching people like Casey from BugCrowd, Mike and Alan at UpGuard, Rory at Propeller, Michael at Morse Micro, and Alexandra and Nic at Workyard turn ideas into global companies has been a joy to see.

Since exiting Accelo last year, I’ve had the privilege to work very closely with the last three cohorts, leading the B2B stream and making it to my first Sydney Demo Day ever earlier this year! And being close to it again with more experience has reminded me that Startmate’s real achievement isn’t the portfolio value; it’s the culture. A community of founders and mentors who see helping others win as part of their own personal mission and higher purpose.

So to the Winter 2025 founders: enjoy Demo Day. You’re stepping into a lineage that’s shaped a generation of Aussie and Kiwi startups. Stay curious, stay close to your customers, and keep paying it forward – it’s what makes this ecosystem so special.

Chapter 4: Building Ascendius in Public with AI

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After two decades building and running technology companies, I wanted to approach Chapter 4 differently – not by forgetting what I’ve learned, but by questioning everything I thought I knew, because I believe with AI, the rules have changed.

That’s what Ascendius is about – and this post is the first in a series about building it. My goal is simple: build a technology company that generates more than $10 million in annual recurring revenue with fewer than ten full-time employees. This isn’t unheard of, but it won’t be easy, and I’m excited to give it a shot!

The Hypothesis

The hypothesis behind Ascendius – the parent company of TeamScore and what I hope will become a family of sibling products – comes down to three ideas.

1. AI can make talented individuals 5x more productive.
While I’m not sure whether it will be 2x or 5x or 10x, I’ve already seen this firsthand through building TeamScore. AI tools make it possible to plan, write, code, and market faster than ever before – with quality that’s not “good enough,” but genuinely impressive. If that compounding advantage continues, we’ll be able to ship world-class products faster and cheaper than previously possible.

2. Post-AI companies have an unfair advantage.
Starting fresh matters. Established companies have to wrestle with technical debt, organizational inertia, and the politics of change. When you start clean, you can design every workflow, system, and even incentive around AI from the beginning. That’s not just an efficiency gain – it’s structural leverage.

3. Solving the CAC crisis is critical.
Even as it’s become cheaper to build software, it’s become harder to get it in front of the users who need it. Inboxes are scorched earth where we hover over the “report spam” button. No one answers a call from a number they don’t already know. The ad duopoly of Google and Meta extracts every last dollar of marginal spend, while Apple’s 30% outrageous “tax” continues to impair innovation.

So despite the cost of creation dropping, the cost of acquisition keeps climbing. That’s a crisis. 

I don’t know how to solve it yet, but I think the answer lies in combining audience-first thinking, cross-selling across a product portfolio, and AI-driven marketing that’s genuinely helpful rather than spammy. It’s one of the puzzles I’m thinking the most about.

The Productivity Promise

There’s no doubt we’re in the upswing of the AI hype curve. Anyone who’s used AI to do their job knows that feeling of awe when they completed a task way faster than before. However, a recent MIT report also found that 95% of corporate AI pilots are failing. But just like when the internet first emerged 30 years ago, it is clear to anyone who’s used it that this technology is powerful and transformative. 

One of the reasons I think big companies are struggling is because they’re trying to eliminate all of a lower-level role before applying the technology further up the expertise stack. This made sense in the industrial era, where robots did rote, repetitive tasks, but in the post-industrial knowledge economy, AI doesn’t completely eliminate one type of job at a time. Instead, it reshapes every job it touches and often has a bigger impact at the non-routine, non-rote work higher up the experience stack. In my experience, it doubles the productivity of almost every knowledge-based role – including up to the CEO and Board.

That’s the unlock. You can use AI for the things you used to hire an analyst, a marketing agency, or even a strategy consultant to do. For a few dollars a month, and instantly. 

AI is an exoskeleton for talented, creative people – not a replacement for them.

The companies seeing results are the ones where people seek the unlock instead of fearing it. Where AI isn’t a threat, but a multiplier.

At Ascendius, I use AI as an active partner for multiple hours every single day. I use it to brainstorm strategy, design architecture, refactor code, and, of course write 3 or more blog posts a week. The productivity gain isn’t about speed alone – it’s about the quality and breadth of what one person can now achieve.

But what I also know first hand is that “vibing” doesn’t work. I’ve been as excited as anyone to see a prototype come to life before my eyes, and the first version of TeamScore was an MVP alpha built super fast. But whether your coding or writing a legal brief or a consulting report, vibing doesn’t work. As a technology product, vibed code is unmaintainable and often insecure. As a marketing and sales tool, set-and-forget AI tools do more harm to brands and products than they save in time. 

However, if you use AI as a multiplier instead of a replacement, it is transformative.

For example, with TeamScore I was able to build a powerful, multi-region product with two dozen connectors in a programming language I didn’t know on a back end I’d never used in <6 months. It is how I was able to create a 30 page go-to-market plan that should take over 3 months in under 3 weeks. It is how I’ve been able to do detailed analysis of data in a couple of days that would have taken a couple of weeks, and of course how I’ve been able to write this and all of my other blog posts over the last two weeks while doing everything else.

That’s the productivity promise – and it’s already real.

The Advantage of a Clean Start

Most established companies are trying to retrofit AI into organizational structures, processes, and policies built for a pre-AI world.

Those structures weren’t designed to resist change – they were designed to manage risk and maximize the consistency of people. But now, every role, policy, and workflow is a piece of friction resisting change whether actively or accidentally. When people evaluate AI through the lens of how to do their job rather than asking whether their job should exist, progress slows to a crawl.

As Upton Sinclair wrote back in 1934,

“It is difficult to get a man to understand something, when his salary depends upon his not understanding it.”

It’s not malice. It’s human nature. It hasn’t changed in the 90+ years since Sinclair wrote it, and it isn’t going to change any time soon. 

For technology companies, the problem runs even deeper than processes, bureaucracy and politics. It’s not just the people – it’s the code.

Joel Spolsky, the doyen of software engineering and founder of StackOverflow and Trello, wrote the canonical warning more than 25 years ago in “Things You Should Never Do, Part I” . Rule number one being never rewrite your software from scratch. It was true then, and mostly still is.

But now established technology companies face a paradox. To take full advantage of AI, they need to modernize their infrastructure, data models, and workflows. Yet for any company more than a few years old, doing so requires breaking Joel’s first rule.

While you can bolt AI onto an old codebase, you’re not going to see many benefits – at least not compared to AI-native tech companies.

All of this gets even harder when the company is owned by private equity – where the plan to flip a company in 3-5 years isn’t compatible with the timeline or investment required to take advantage of AI. The founders are gone, the MBAs are in charge, and while they’re good at doing acquisitions and pricing strategy, product and engineering innovation isn’t usually their sport.

That’s why starting fresh is such a competitive advantage. In addition to being able to harness powerful tech like Cursor or Claude Code because your code isn’t legacy spaghetti, there’s also no team defending the old system, no hierarchy to preserve, no compliance department standing in the way of experimentation.

And it’s why this era feels so exciting.

Many of my friends who’ve exited their companies are back at it again – not because they need to, but because it’s rare to get both the experience of having built before and the freedom of a blank slate.

In a world of massive change, that’s the sweetest combination there is.

Solving the CAC Crisis

While AI promises to accelerate the product engine of a tech company, customer acquisition cost (CAC) remains a massive choke pointw

For years, the cost to build a new software product has fallen. But the cost to find customers has continued to increase.

Google and Meta’s action-based advertising duopoly soaks up every incremental dollar of acquisition budget, while Apple’s evil 30% tax and self-preferencing stifles innovation. Old sales playbooks continue to see diminishing returns: the inbox is scorched earth with cold emails quickly getting the “report spam” click, no one answers calls from phone numbers they don’t already know anymore because of scammers.

At the same time, AI will make it even cheaper to build new products. The result is a flood of competition fighting for the same attention, in the same channels, with the same tools.

That’s the CAC crisis.

If this era is going to produce a new generation of durable software businesses, we’ll need new distribution models to match, and being 5x as efficient in your biggest cost – payroll – provides the ability to invest in a better product at a better price along with new go-to-market tactics. These could include audience-driven portfolios, value-based bundling, or deeply automated go-to-market loops that are personalized instead of pushy. There’s also promise in new players providing new paths to discovery – as long as we can keep the AI-slop at bay.

I don’t have the full answer yet. But it’s one of the most interesting challenges in modern entrepreneurship – and I’m thinking about it every day.

Let’s Go!

We’re living through the biggest change in technology since the internet went mainstream 30 years ago – and it might prove even more consequential than that.

For builders, it’s a once-in-a-generation opportunity to rethink everything – not just products, but companies themselves.

So that’s what I’m doing. And if you’re building too, I hope you’ll come along for the ride.

The Analytics Trap

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Data’s Siren Song

More data and better analytics always sound like a good thing. They call to tech founders tempted to build analytics tools, and to ambitious leaders who want to be data-driven. Data promises clarity, control, and confidence – right?

Unfortunately, while they’re exciting to build and attractive to buy, almost no one really uses analytics tools unless analyst is literally in their job title.

I’ve seen it over and over again – and it’s how smart people get caught in the Analytics Trap. I was recently mentoring an awesome startup, and gave them this advice:

I think analytics as a category is one of the worst for any tech company (prob second only to devtools). It is because us nerds love to build cool stuff with numbers, databases and reports. And the market says “we need this, it will make us better if we’re data driven”. And then no one uses it. This is because people are already overloaded with information and trust their intuition more than data. Also, if data shows they’ve spent their career being lucky, not skilled/smart, that is a big blow to your sense of self. So, the space gets massive effort by smart people, selling the products is a big uphill push, and then the NPS and ROI suck. Avoid analytics.

Why We Keep Falling for It

For founders, analytics feels like the purest kind of product. It’s logical. Quantitative. Defensible. You can point to a dashboard and say, “Look – truth!”

For buyers, it feels like leadership. “We’re data-driven” is one of those phrases that looks great in board decks and job descriptions.

The problem is that everyone’s buying the same illusion: the belief that more data automatically leads to better decisions. It can, but it often doesn’t – not when finding the answers in the data requires more work and slower decisions.

Most people have learned to trust their intuition and experience because, most of the time, they’re making familiar decisions. They only turn to data when they’re facing a brand-new question – and that doesn’t happen nearly as often as we like to imagine.

The Confidence Illusion

Most people say they want data. But what they actually want is confidence.

They want to feel sure they’re doing the right thing, and while a dashboard can help, it isn’t going to take the fall if it is the wrong decision.

Unfortunately, while analytics can boost confidence around a decision, but it always gives you homework.

To be data-driven, you have to go looking for the insight, make time to interpret it, and then convince others to act on it. It’s valuable work – but it’s still work. And when the day fills up with meetings, Slack pings, and fires to put out, the homework always loses.

The Toothbrush Lesson

I first saw this twenty years ago, working with Google not long after they acquired Urchin, which became Google Analytics.

At trade shows, Google gave away bright orange toothbrushes printed with:
“Google Analytics: Use Twice a Day.”

Even Google knew analytics was homework.

If the world’s most data-driven company had to remind people to use its analytics product, it wasn’t a design flaw – it was human nature. Reflection is optional, and optional work never wins against urgent work.

Why Founders and Buyers Both Get Caught

Founders and buyers fall into the trap from opposite sides.

Founders overestimate rational behavior: “If we show people the data, they’ll act.”
Buyers overestimate their own discipline: “This time, we’ll actually use it.”

Both underestimate the cost of context-switching – of stopping to analyze, interpret, and decide. It’s not that people don’t value data; they just don’t prioritize it once the real world starts screaming for attention.

So the dashboards sit idle, and the engagement graphs slide down.

When Analytics Becomes Homework

A few power users go deep. After the implementation and training phase, users rarely log in. Beautiful emailed reports sit unopened, ignored or unsubscribed.

The product team notice and understandably focus on building what the handful of active users want. This 5% of power users want more filters, more charts, more advanced reports. The product gets smarter, but also harder to use, which means but the audience gets smaller.

You end up with a heads up display for highly-trained pilots instead of a useful tool for managers.

And that’s the fatal flaw: if the output of your product is a report, you’re in trouble. Reports make people stop to think; great products make people take action.

Escaping the Trap: Analytics in the Workflow

While most analytics tools fail, some succeed – usually because they’re part of an actual workflow.

Mixpanel works because you analyze to act. You build audiences, trigger messages, measure results. The analysis isn’t the end or something you do “when you have time”; it’s the source of the activity. The same is true for many fraud and security tools – they proactively tell you what’s happening and what you need to do.

PostHog solved it differently. They accepted that most users wouldn’t engage daily, so they built an open-source model where 95% can use it free and the 5% who care most fund the business. They didn’t fix behavior; they fixed the economics.

I loved using Heap at my last company, but PostHog is the clear winner.

The lesson? Analytics only works when it’s directly connected to action – or when the business model doesn’t depend on everyone logging in.

Intelligence, Not Analytics

A friend told me recently he’d been using ActivTrak for months – or rather, he had it installed for six months but hadn’t really used it.

After hearing about TeamScore, he decided to dig back into the tool he already had, spending hours exploring the reports. He found real value – insights he wished he’d seen sooner. But it also demonstrated the trap: value that only appears after you do the extra work.

We were talking about that experience, and he said ActivTrak had so much data – but what he really wanted was something to tell him what to look at.

I showed him a beta of our daily AI summary in Slack, and he both laughed and winced:
“That’s exactly what I needed all this time.”

That’s the difference between analytics and intelligence. One demands your attention to get any value. The other does some of the thinking for you.

The Hard Truth

Analytics promises clarity, but most people just want confidence.

The gap between what we say and what we do isn’t irrational – it’s human. Being data-driven sounds great until you realize it means assigning yourself more homework.

It is still early for the startup I was helping, but a week and a bit later they came back with this:

Hopefully this perspective helps other operators, too!

Kicking off Chapter 4

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When I sold Accelo a year and a half ago, I promised myself something I’d never really given before: a break.

Truth is, I wasn’t great at it. My family will tell you I failed miserably at “learning how to sit still.”

But spending more time with them made me realize something important: if I was going to be the father I want to be, I needed to get into shape.

My own dad died young, and I couldn’t keep being unfit and overweight if I wanted to be around to walk my three girls down the aisle one day. So I did something about it. I dropped over 18kg (45 pounds), got fit, and rediscovered what it feels like to have a body that can keep up with my ambitions.

Back to Building

By the end of last year, I felt the itch again. I wanted to get back into technology. I wanted to build. And I wanted to really get my hands dirty with AI – to figure out what’s real vs hype, and harness it to do things differently for my next startup.

Rather than following tutorials or vibe coding yet another photo app, I decided to actually build a product that I wish I’d always had when running Accelo. At the beginning of this year, I began building TeamScore.

The Problem That Wouldn’t Let Go

Running Accelo, one of the toughest challenges my managers and I faced was managing a remote team – especially one that wasn’t hired to be remote.

When you’re in the office, you have peripheral vision. You see who’s busy, who’s stuck, and where things are slipping. Remote took that away.

I saw that managers were left with three bad options:

  1. Turn into a micromanaging tyrant, staring at the color of lights on Slack, scheduling 5pm Friday meetings and pestering for constant updates. That didn’t fly with us.
  2. Hope for the best, flying blind until problems blew up. And since hope isn’t a plan, surprise surprise, blew up they did.
  3. Install invasive monitoring software on employee devices which that monitors mouse movements, takes screenshots, records browser history and more. While it provided data, spyware is kryptonite for the superheroes on your team. Killing their passion just to get a clearer lens on performance never felt like a trade worth making, and treats your best people like suspects.

That pain stuck with me. I could see remote wasn’t going away, but the more I talked to other entrepreneurs, the more it was clear that remote wasn’t really working, either.

So I decided to build TeamScore.

What I’m Building

TeamScore is a zero-footprint, instant-setup platform that transforms the cloud security logs companies already have into AI-powered insights for managers.

No spyware or agents on employee devices. Just clear, actionable visibility into what your team is actually working on – so you can make remote work.

It’s the tool I wish I’d had, and the one I believe many managers and entrepreneurs need today. And today it is being launched into public beta – join the waitlist at app.teamscore.io/sign-up.

I was determined to make Chapter 4 different – drawing on the lessons of helping more than 5,000 businesses succeed in my prior startups, but not just running the same playbook again.

AI has changed the game. Its rapid progress makes it possible to build technology faster, leaner, and more affordably than ever before.

That’s why the goal for TeamScore is ambitious but focused: to build a $10M ARR startup serving over 100,000 businesses while keeping headcount to under ten full-time employees. Not by cutting corners, but by building smart, automating what can be automated, and keeping the team razor-focused on what matters.

And TeamScore is just the start. My vision is to launch a family of products designed to help entrepreneurs and their businesses thrive—ideally one new venture each year. TeamScore is the first step, and it won’t be the last.

It’s been an incredible journey so far, and I’ll have plenty more to share in the months ahead. But today I’m excited to say: TeamScore is ready for public beta.

Read the TeamScore launch post here.

Closing Thoughts

Time off reminded me I’m bad at sitting still. Getting healthy reminded me that discipline pays off. And starting again reminded me that the best problems to solve are the ones you’ve lived yourself.

So here we go. Round 4. Let’s gooooo!